Working With Data

Every once in a while I read something that is so insightful, so clearly written and so well documented that it enters my own personal pantheon of “Best Ever” documents. I recently added a new, simply divine article titled Best Practices for Scientific Computing and hope that everyone reading this post also takes the time to read that article. I’m including the outline here only to encourage you to read the article in it’s entirety.  It is extremely well written.   read more …

The post titled Installing Packages described the basics of package installation with R.  The process is wonderfully simple when everything goes well.  But it can be maddening when it does not.  Error messages give a hint as to what went wrong but do not necessarily tell you how to resolve the problem.  This post will collect some of the error messages we’ve encountered while installing R packages and describe the reasons for the error and the workarounds we’ve found.   read more …

Yes, this post condenses 50+ hours of learning into a 15 minute tutorial.  Read ‘em and weep.  (That is, you read while I weep.)   read more …

In two previous posts we described how R can call C code with .C() and the more complex yet more robust option of calling C code with .Call().  Here we will describe how the Rcpp package can be used to greatly simplify your C code without forcing you to become expert in C++.   read more …

In an introductory post on R APIs to C code, Calling C Code ‘Hello World!’, we explored the .C() function with some ‘Hello World!’ baby steps.  In this post we will make a leap forward by implementing the same functionality using the .Call() function.   read more …